This review addresses the urgent need for validated tests of consciousness (C-tests) applicable to a wide range of systems, including humans, nonhuman animals, artificial intelligence, and neural organoids. The authors identify key challenges in developing such tests and propose a multidimensional framework for classifying and validating them. They argue that C-tests should focus on detecting subjective, qualitative experience (phenomenal consciousness) rather than other capacities like intelligence or self-regulation. The review highlights the importance of distinguishing between different types of validity (construct, content, criterion, and face validity) and emphasizes the need for a robust validation process. The authors propose a four-dimensional space for C-tests, considering factors such as target population, specificity, sensitivity, and rational confidence. They suggest that C-tests should be validated through an iterative natural kind (NK) strategy, treating consciousness as a natural kind and using empirical data to refine and revise pre-theoretical judgments. This approach allows for the development of C-tests that can be applied to a wide range of populations, from humans to AI systems. The review also discusses the challenges of validating C-tests, including the generalization problem and the potential for circularity in theory-based validation. It advocates for a bootstrapping approach, starting with populations closest to humans and gradually extending to more alien systems. The authors emphasize the importance of considering the moral implications of consciousness, as determining whether a system is conscious has significant ethical and practical consequences. The review concludes by highlighting the need for a multidimensional framework that can guide the development and validation of C-tests, ensuring that they are both scientifically rigorous and ethically sound. It calls for continued research and collaboration across disciplines to address the complex challenges of defining and measuring consciousness in a wide range of systems.This review addresses the urgent need for validated tests of consciousness (C-tests) applicable to a wide range of systems, including humans, nonhuman animals, artificial intelligence, and neural organoids. The authors identify key challenges in developing such tests and propose a multidimensional framework for classifying and validating them. They argue that C-tests should focus on detecting subjective, qualitative experience (phenomenal consciousness) rather than other capacities like intelligence or self-regulation. The review highlights the importance of distinguishing between different types of validity (construct, content, criterion, and face validity) and emphasizes the need for a robust validation process. The authors propose a four-dimensional space for C-tests, considering factors such as target population, specificity, sensitivity, and rational confidence. They suggest that C-tests should be validated through an iterative natural kind (NK) strategy, treating consciousness as a natural kind and using empirical data to refine and revise pre-theoretical judgments. This approach allows for the development of C-tests that can be applied to a wide range of populations, from humans to AI systems. The review also discusses the challenges of validating C-tests, including the generalization problem and the potential for circularity in theory-based validation. It advocates for a bootstrapping approach, starting with populations closest to humans and gradually extending to more alien systems. The authors emphasize the importance of considering the moral implications of consciousness, as determining whether a system is conscious has significant ethical and practical consequences. The review concludes by highlighting the need for a multidimensional framework that can guide the development and validation of C-tests, ensuring that they are both scientifically rigorous and ethically sound. It calls for continued research and collaboration across disciplines to address the complex challenges of defining and measuring consciousness in a wide range of systems.